Handbook of machine learning (Record no. 35250)
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fixed length control field | 06485cam a2200277 i 4500 |
003 - CONTROL NUMBER IDENTIFIER | |
control field | CUTN |
005 - DATE AND TIME OF LATEST TRANSACTION | |
control field | 20210806151226.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 180530m20199999njua b 001 0 eng |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
International Standard Book Number | 9789813271227 (hc : alk. paper : v. 1) |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
International Standard Book Number | 9813271221 (hc : alk. paper : v. 1) |
041 ## - LANGUAGE CODE | |
Language | English |
042 ## - AUTHENTICATION CODE | |
Authentication code | pcc |
082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
Classification number | 006.31 |
Edition number | 23 |
Item number | MAR |
100 1# - MAIN ENTRY--PERSONAL NAME | |
Personal name | Marwala, Tshilidzi, |
245 10 - TITLE STATEMENT | |
Title | Handbook of machine learning |
Statement of responsibility, etc | Tshilidzi Marwala (University of Johannesburg, South Africa). |
300 ## - PHYSICAL DESCRIPTION | |
Extent | volumes : |
Other physical details | illustrations ; |
Dimensions | 25 cm |
505 0# - FORMATTED CONTENTS NOTE | |
Contents | volume 1. Foundation of artificial intelligence -- |
Title | Contents<br/>Preface<br/>About the Author<br/>Acknowledgements<br/>1. Introduction<br/>1.1 Introduction<br/>1.2 Time Domain Data<br/>1.2.1 Average<br/>1.2.2 Variance<br/>1.2.3 Kurtosis<br/>1.3 Frequency Domain<br/>1.4 Time–Frequency Domain<br/>1.5 Fractals<br/>1.6 Stationarity<br/>1.7 Common Mistakes on Handling Data<br/>1.8 Outline of the Book<br/>1.9 Conclusions<br/>References<br/>2. Multi-layer Perceptron<br/>2.1 Introduction<br/>2.2 Multi-layer Perceptron<br/>2.3 Training the Multi-layered Perceptron<br/>2.4 Back-propagation Method<br/>2.5 Scaled Conjugate Method<br/>2.6 Multi-layer Perceptron Classifier<br/>2.7 Applications to Economic Modelling<br/>2.8 Application to a Steam Generator<br/>2.9 Application to Cylindrical Shells<br/>2.10 Application to Interstate Conflict<br/>2.11 Conclusions<br/>References<br/>3. Radial Basis Function<br/>3.1 Introduction<br/>3.2 Radial Basis Function<br/>3.3 Model Selection<br/>3.4 Application to Interstate Conflict<br/>3.5 Call Behaviour Classification<br/>3.6 Modelling the CPI<br/>3.7 Modelling Steam Generator<br/>3.8 Conclusions<br/>References<br/>4. Automatic Relevance Determination<br/>4.1 Introduction<br/>4.2 Mathematical Basis of the Automatic Relevance Determination<br/>4.2.1 Neural networks<br/>4.2.2 Bayesian framework<br/>4.2.3 Automatic relevance determination<br/>4.3 Application to Interstate Conflict<br/>4.4 Applications of ARD in Inflation Modelling<br/>4.5 Conclusions<br/>References<br/>5. Bayesian Networks<br/>5.1 Introduction<br/>5.2 Neural Networks<br/>5.3 Hybrid Monte Carlo<br/>5.4 Shadow Hybrid Monte Carlo (SHMC) Method<br/>5.5 Separable Shadow Hybrid Monte Carlo<br/>5.6 Comparison of Sampling Methods<br/>5.7 Interstate Conflict<br/>5.8 Conclusions<br/>References<br/>6. Support Vector Machines<br/>6.1 Introduction<br/>6.2 Support Vector Machines for Classification<br/>6.3 Support Vector Regression<br/>6.4 Conflict Modelling<br/>6.5 Steam Generator<br/>6.6 Conclusions<br/>References<br/>7. Fuzzy Logic<br/>7.1 Introduction<br/>7.2 Fuzzy Logic Theory<br/>7.3 Neuro-fuzzy Models<br/>7.4 Steam Generator<br/>7.5 Interstate Conflict<br/>7.6 Conclusions<br/>References<br/>8. Rough Sets<br/>8.1 Introduction<br/>8.2 Rough Sets<br/>8.2.1 Information system<br/>8.2.2 The indiscernibility relation<br/>8.2.3 Information table and data representation<br/>8.2.4 Decision rules induction<br/>8.2.5 The lower and upper approximation of sets<br/>8.2.6 Set approximation<br/>8.2.7 The reduct<br/>8.2.8 Boundary region<br/>8.2.9 Rough membership functions<br/>8.3 Discretization Methods<br/>8.3.1 Equal-width-bin (EWB) partitioning<br/>8.3.2 Equal-frequency-bin (EFB) partitioning<br/>8.4 Rough Set Formulation<br/>8.5 Rough Sets vs. Fuzzy Sets<br/>8.6 Multi-layer Perceptron Model<br/>8.7 Neuro-rough Model<br/>8.7.1 Bayesian training on rough sets<br/>8.7.2 Markov Chain Monte Carlo (MCMC)<br/>8.8 Modelling of HIV<br/>8.9 Application to Modelling the Stock Market<br/>8.10 Interstate Conflict<br/>8.11 Conclusions<br/>References<br/>9. Hybrid Machines<br/>9.1 Introduction<br/>9.2 Hybrid Machine<br/>9.2.1 Bayes optimal classifier<br/>9.2.2 Bayesian model averaging<br/>9.2.3 Bagging<br/>9.2.4 Boosting<br/>9.2.5 Stacking<br/>9.2.6 Evolutionary machines<br/>9.3 Theory of Hybrid Networks<br/>9.3.1 Equal weights<br/>9.3.2 Variable weights<br/>9.4 Condition Monitoring<br/>9.5 Caller Behaviour<br/>9.6 Conclusions<br/>References<br/>10. Auto-associative Networks<br/>10.1 Introduction<br/>10.2 Auto-associative Networks<br/>10.3 Principal Component Analysis<br/>10.4 Missing Data Estimation<br/>10.5 Genetic Algorithm(GA)<br/>10.6 Machine Learning<br/>10.7 Modelling HIV<br/>10.8 Artificial Beer Taster<br/>10.9 Conclusions<br/>References<br/>11. Evolving Networks<br/>11.1 Introduction<br/>11.2 Machine Learning<br/>11.3 Genetic Algorithm<br/>11.4 Learn++ Method<br/>11.5 Incremental Learning Method Using Genetic Algorithm (ILUGA)<br/>11.6 Optical Character Recognition (OCR)<br/>11.7 Wine Recognition<br/>11.8 Financial Analysis<br/>11.9 Condition Monitoring of Transformers<br/>11.10 Conclusions<br/>References<br/>12. Causality<br/>12.1 Introduction<br/>12.2 Correlation<br/>12.3 Causality<br/>12.4 Theories of Causality<br/>12.4.1 Transmission theory of causality<br/>12.4.2 Probability theory of causality<br/>12.4.3 Projectile theory of causality<br/>12.4.4 Causal calculus and structural learning<br/>12.4.5 Granger causality<br/>12.4.6 Structural learning<br/>12.4.7 Manipulation theory<br/>12.4.8 Process theory<br/>12.4.9 Counter factual theory<br/>12.4.10 Neyman–Rubin causal model<br/>12.4.11 Causal calculus<br/>12.4.12 Inductive causation (IC)<br/>12.5 How to Detect Causation?<br/>12.6 Causality and Artificial Intelligence<br/>12.7 Causality and Rational Decision<br/>12.8 Conclusions<br/>References<br/>13. Gaussian Mixture Models<br/>13.1 Introduction<br/>13.2 Gaussian Mixture Models<br/>13.3 EM Algorithm<br/>13.4 Condition Monitoring: Transformer Bushings<br/>13.5 Condition Monitoring: Cylindrical Shells<br/>13.6 Condition Monitoring: Bearings<br/>13.7 Conclusions<br/>References<br/>14. Hidden Markov Models<br/>14.1 Introduction<br/>14.2 Hidden Markov Models<br/>14.3 Condition Monitoring: Motor Bearing Faults<br/>14.4 Speaker Recognition<br/>14.5 Conclusions<br/>References<br/>15. Reinforcement Learning<br/>15.1 Introduction<br/>15.2 Reinforcement Learning: TD-Lambda<br/>15.3 Game Theory<br/>15.4 Multi-agent Systems<br/>15.5 Modelling the Game of Lerpa<br/>15.6 Modelling of Tic–Tac–Toe<br/>15.7 Conclusions<br/>References<br/>16. Conclusion Remarks<br/>16.1 Summary of the Book<br/>16.2 Implications of Artificial Intelligence<br/>References<br/>Index |
520 ## - SUMMARY, ETC. | |
Summary, etc | This is a comprehensive book on the theories of artificial intelligence with an emphasis on their applications. It combines fuzzy logic and neural networks, as well as hidden Markov models and genetic algorithm, describes advancements and applications of these machine learning techniques and describes the problem of causality. This book should serves as a useful reference for practitioners in artificial intelligence. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Machine learning. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name as entry element | Artificial intelligence. |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Source of classification or shelving scheme | Dewey Decimal Classification |
Koha item type | Reference Books |
100 1# - MAIN ENTRY--PERSONAL NAME | |
Dates associated with a name | 1971- |
Relator term | author. |
504 ## - BIBLIOGRAPHY, ETC. NOTE | |
Bibliography, etc | Includes bibliographical references and index. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
9 (RLIN) | 4 |
906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) | |
a | 7 |
b | cbc |
c | orignew |
d | 1 |
e | ecip |
f | 20 |
g | y-gencatlg |
Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Location | Shelving location | Date of Cataloging | Total Checkouts | Full call number | Barcode | Date last seen | Price effective from | Koha item type |
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Dewey Decimal Classification | Non-fiction | CUTN Central Library | CUTN Central Library | Reference | 06/07/2021 | 006.31 MAR | 44010 | 06/07/2021 | 06/07/2021 | Reference Books |